Hitherto, various kinds of tools have been developed by applying an optimization technique of selecting the best one from among plural alternatives under predetermined conditions. In most of the above optimization tools, an output optimization result does not match with an actual operation or needs of a user. In short, the optimization tools give a solution different from what a user demands.
To obtain a solution that a user requires, parameters (weights assigned to evaluation items for outputting an optimization result) could be adjusted. However, it is important to understand the algorithm of each optimization tool for adjusting parameters. In general, it is not easy for a user to understand even the meaning of the parameters. In this case, however, each user needs to manually adjust the parameters.
For example, as for evaluation items directly related to a business such as cost and lead time, a user can adjust weights of these items. However, it is difficult for a user to adjust parameters for evaluating the working time of a plurality of vehicles, that is, determining whether to keep the balance therebetween or not.
To adjust such parameters, a user should be familiar with both of an operation and an optimization tool. Thus, in many cases, a user makes adjustment with the help of an engineer. It is difficult for a user to directly adjust parameters in some cases. Further, the number of optimization evaluation items increases along with an increase in the number of constraints set by a user, which makes it more difficult to adjust parameters.
On the other hand, another approach is for applying an optimization result to an actual operation after a user manually adjusts the optimization result, not adjusting parameters. For example, in general schedule optimization applications or the like, if a user is not satisfied with an output result, the optimum solution itself is edited as the user desires to satisfy user's requests instead of adjusting parameters. This approach adjusts the output result, not the parameters that a user cannot easily understand.
However, if data is partially corrected, an optimum solution of the entire data is changed, so the whole schedule needs to be revised. Hence, there is no point to execute optimization in a system since the user needs to edit the solution each time, which considerably lowers operation efficiency.
In such circumstances, a method for solving the problems involved in the multi-objective optimization has been proposed (for example, Patent Documents 1 and 2 described below). According to the method disclosed in Patent Document 1, in the case of determining values of plural components in a target system with an optimization method so as to obtain desired characteristics of the system, individual calculating means for finding a solution that satisfies each evaluation function is provided, and plural optimum solutions are obtained using the individual calculating means. According to the method disclosed in Patent Document 2, an initial solution S is generated to determine a search parameter for determining probability of transition to a new solution and then, a solution S′ approximate to the solution S is obtained to determine probability of transition to the solution S′ for each objective function to determine the total probability of transition to the solution S′. Then, if the total transition probability is larger than a threshold value, the solution S′ is set as a new solution S, and the step of determining a search parameter and subsequent steps are repeated. If the probability is smaller than a threshold value, the solution S′ is not set as a new solution S, and the step of determining a search parameter and subsequent steps are repeated.
[Patent Document 1]
Japanese Patent Application Publication No. 10-40286
[Patent Document 2]
Japanese Patent Application Publication No. 7-44611